Coronavirus term paper

EF_Kerns2008CONA

Covid-19 Assignment Just over a year ago, the Covid-19 (Coronavirus) disease was declared a pandemic. Across the planet, the ensuing shutdown of travel, public gatherings, business activities, and social services brought on a number of psychosocial consequences. This psychological burden includes core affective states of anxiety, depression and anger as well as boredom, loneliness, confusion, irritability, frustration, and (ultimately) fatigue. In a 5-7page, double-spaced paper (plus references and cover page), relate these psychological effects of the pandemic to two theoretical concepts we have covered in this course: (i) the Core of Negative Affect (CONA) and (ii) the General Adaptation Syndrome (GAS). Thirdly, explain how such affective states can in turn influence susceptibility and response to (Covid-19) infection as they have in the case of cancer and HIV/AIDS. Cite relevant scientific evidence. Published research on Covid-19 is now available in the Coronavirus Research Database accessible electronically through the UTSA library system

https://libguides.utsa.edu/az.php?a

＝c For information on the core of -ve affect (CONA) see the attached article: Fernandez, E. & Kerns, R.D. (2008). Anxiety, depression, and anger: The core of negative affect in medical populations. In G. J Boyle, G. Matthews & D. Saklofske (Eds.). International Handbook of Personality Theory and Testing: Vol. 1: Personality Theories and Models (pp. 659- 676). London: Sage Publications. For the General Adaptation Syndrome (GAS), basic info is given in the Workbook chapter and corresponding lecture on Stress. Basic information on psychological factors in cancer and HIV/AIDS is found in the relevant workbook chapters and lectures on these topics. Additional sources can be found in the PsycInfo database also accessible electronically through the UTSA library system. Format the paper according to the American Psychological Association (APA) publication manual 7th edition, with special attention to correct format for citations and references. This is not an experimental paper or a report of a study done by you; rather it’s more like a narrative review of research published by others. Use headings/subheadings to give organization to your paper. This project is worth 30% of your overall grade in this class. It will be evaluated on the following criteria: CONTENT Brief desсrіption of Covid-19 emotional effects Relating these Covid-19 effects to theoretical concepts of CONA and GAS Explaining how affective states can influence susceptibility/response to Covid-19, as in the case of cancer and HIV/AIDS Accurate reporting of evidence from relevant sources STYLE & format Language: diction, syntax, grammar, spelling Organization: coherence and use of headings APA format 

Anxiety, Depression, and Anger:
Core Components of Negative
Affect in Medical Populations

Ephrem Fernandez and Robert D. Kerns

NEGATIVE AFFECT

Within psychology, the term ‘affect’ has
evolved out of restricted usages within psy-
choanalysis and clinical psychiatry into a
general term that refers to any kind of subjec-
tive feeling (Tomkins, 1962). Imposed with
a metaphor from chemistry, affect is now
regarded as either positive or negative in
valence, the former implying pleasant
feelings and the latter implying unpleasant
feelings. Other terms used interchangeably
with negative affect are ‘dysphoria’ and
‘distress’, though sometimes the words
‘stress’ and ‘suffering’ are also used to
loosely suggest negative affect. The main
point of consensus is that negative affect
refers to any form of subjective feeling that is
experienced as unpleasant in quality. Such
unpleasantness can also vary quantitatively,
that is, on a dimension of intensity. This
common property of affect (be it positive
or negative) is also labeled as activation or
arousal.

Various types of negative affect have
appeared in the diagnostic criteria for psychi-
atric disorders (e.g. schizophrenia, post-trau-
matic disorder, borderline personality
disorder, obsessive-compulsive disorder)
partly because ‘distress’ is regarded as one of
the associated features of all mental disorders
(American Psychiatric Association, 2000).
Yet people with somatic complaints of med-
ical disease have rarely been examined
for clinically significant levels of negative
affect. This is probably an outcome of the
mind–body dualism that has infused the
health sciences for centuries. In this chapter,
we report on some of the recent findings that
do point to a spectrum of negative affect in
medical populations. Supported by theoreti-
cal foundations and empirical data, we direct
our attention to three specific types of nega-
tive affect: anger, fear, and sadness, or their
clinical equivalents of anger, anxiety, and
depression, respectively. This, we call the
core of negative affect (CONA). With refer-
ence to medical populations, we focus on the

32

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three highly prevalent ailments in developed
as well as developing countries of the world:
cardiovascular disease (CVD), cancer, and
HIV/AIDS. Furthermore, we draw parallels
between the CONA as manifested in these
populations and CONA as already researched
in one population: patients who suffer
from pain.

ANXIETY, DEPRESSION, AND ANGER

Anxiety and depression have often been
studied as twin features of negative affect,
but more recently, anger has been introduced
as a close relative to form a new triad of neg-
ative affect. Barlow (1991) made a bridge
between the experimental psychology of
emotions and the clinical psychology of
emotional disorders, by postulating how fear,
sadness, and anger lie at the root of anxiety,
depression, and anger disorders. Spielberger
et al. (1995) grouped depression, anxiety,
and anger under the label of ‘emotional vital
signs’, a construct later echoed by Ghosh and
Puja (2004). Examining pain patients as a
‘test population’, Fernandez et al. (1999) and
Fernandez (2002) showed that there is ample
empirical evidence to position anxiety,
depression, and anger within the core of
negative affect.

Vital signs

The idea of emotional vital signs was origi-
nally spun out of Spielberger’s view that anx-
iety was analogous with heart rate, anger
with blood pressure, and depression with
fever. The analogy may not be perfect since
the term ‘vital signs’ as used in medicine
refers to objective signs that the systems of
the body (required to keep a person alive) are
in working order or normal. When measured
values for respiration, heart rate, blood pres-
sure, and temperature are zero, the person is
evidently dead; when they reach a certain norm
for the species, the organism is essentially

alive and well. In the case of anxiety, depres-
sion, and anger, zero values would point to
healthy emotional functioning, while high
values, though not necessarily a sign that life
is threatened, do raise concerns for the well-
being of oneself or others. Profound depres-
sion could forebode suicidality, extreme
anger could potentiate acts of destruction,
and high-grade fear could be crippling or dis-
abling. In that sense, if one were to select
three affective types as indices of a person’s
emotional health, anxiety, depression, and
anger would probably be the most appropriate
choices.

The core

In using the word ‘core’ to refer to the group
of three negative affects, we do not imply
anything that resides deep within the individ-
ual. These subjective feelings are not neces-
sarily hidden as part of an individual’s inner
life. In fact, they are quite open to observa-
tion and measurement. It is their ubiquity and
functional significance that earns them mem-
bership within the core of negative affect.
This kind of pervasiveness and importance is
also captured in the common adage that
depression is the common cold of psychiatry,
the notion in much of psychology that anxiety
is inherent in neuroses if not in our very exis-
tence as humans, and the vast and recurrent
media coverage of acts of anger and rage.

Evolutionary roots

The clinical syndromes of anxiety, depres-
sion, and anger are rooted in fear, sadness,
and anger, respectively. These three discrete
emotions play a primordial and universal role
in the defense against aversive stimuli. Fear,
for instance, is regarded as the most basic of
all emotions because it motivates escape or
avoidance from predators or other insur-
mountable threats, thereby being crucial for
survival. As Marks puts it ‘Fear is a vital
evolutionary legacy … Without fear, few

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would survive long under natural conditions’
(1987: 3). Anger is a twin emotion of fear in
the defense against aversive stimuli. Thus
Walter Cannon (1929) coined the term ‘fight
or flight’ to refer to the twin options of fleeing
out of fear or fighting out of anger during an
emergency. Inasmuch as anger mobilizes the
organism to retaliate in the face of provocation
or assault, it promotes survival.

Surprisingly omitted from evolutionary
accounts of basic emotion is sadness. This
emotion may be viewed as a third option in
the repertoire of responses toward threat or
attack. When escape is not possible, when
retaliation is not feasible, and the prospect of
defeat is looming, then sadness is the emo-
tion that arises in the service of the next
most appropriate response of yielding or
submission. A variant of it is what Seligman
and colleagues term ‘learned helplessness’
(Peterson et al., 1993; Seligman, 1972).
Buerki and Adler simply call it ‘giving up’ in
order to conserve resources:

If a person has experienced certain situations, in
which fight or flight was impossible or of no avail,
he or she might react with conservation–with-
drawal when exposed anew … Conservation–with-
drawal is primarily a biological reaction pattern,
the counterpart of Cannon’s fight–flight reaction.
Both reaction patterns are directed toward adapta-
tion to stressful situations. They are aimed at self-
protection and self-preservation. Fight–flight
attempts to reach its goal by engaging, conserva-
tion–withdrawal by disengaging and saving of
energy. (2005: 5–6)

In the face of an overwhelming offensive,
fighting would be a waste of resources if not
an acceleration toward death. Similarly,
when fighting or fleeing are not viable
options in the face of overwhelming adver-
sity, the emotion is likely to be sorrow and
dejection which primes the individual to
yield or surrender.

Physiological mechanisms

It has been portrayed that certain emotions
have biochemical commonalities such as
hormones and neurotransmitters (serotonin,

dopamine), and involve the same brain struc-
tures. The evidence for this has been highly
conflicting and no attempt will be made to
review these findings here. Besides, it is not
necessary to show biochemical specificity to
justify the existence of different emotions or
to show biochemical commonality to argue
for the similarity of emotions.

What is relatively clear is that anger and
fear involve the hypothalamic–pituitary axis
in order to mobilize the organism toward vig-
orous action of fight or flight. However, sym-
pathetic reactivity is not only the result of
negative affect but can be even greater during
positive affect (Heponiemi et al., 2006).
Also, depression is the one component of
negative affect that is least likely to involve
sympathetic activation, and that makes sense
because the goal in depression is not one of
action as much as inaction.

Recently, Ryff et al. (2006) found that
anxiety and anger had more in common with
regard to biological correlates. Women with
an average age of 74 years old completed
psychometric tests of anxiety, depression,
and anger in addition to providing urine and
blood samples on multiple occasions. It was
found that traits of anxiety were negatively
associated with systolic blood pressure
(SBP) and positively associated with glyco-
sylated hemoglobin. Traits of anger were
inversely correlated with SBP and positively
associated with glycosylated hemoglobin.
Depression did not have any significant asso-
ciations with the above biological correlates
but was positively associated with weight.

AFFECTIVE FORM

As pointed out earlier, research has resound-
ingly demonstrated that affect can be charac-
terized in terms of valence and intensity. In
other words, it can be distinguished qualita-
tively as well as quantitatively. Being high in
affective arousal says nothing about whether
the person is elated or upset, just as being
low in emotional arousal leaves open the

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possibility that the person may be gloomy or
just glad. In addition to valence and intensity,
affect can also be described in terms of form.
By this we mean that affect (which we intro-
duced as a general term) can assume differ-
ent configurations depending on its patterns
of occurrence.

State versus trait

One binary distinction, now popular in psy-
chology, is between affect as a momentary
state versus affect as an enduring trait. Thus,
the anger a person experiences can be
qualified in terms of whether it is a passing
event or a habitual occurrence. Most of the
effort in making this distinction is credited to
Spielberger and colleagues who first pub-
lished the state–trait anxiety scale (STAI;
Spielberger et al., 1977), then the state–
trait anger expression inventory (STAXI;
Spielberger, 1988), and more recently, the
state–trait depression scale (STDS; Krohne
et al., 2002). In doing so, they have proposed
that affective quality be distinguished
according to whether it is a state happening
‘right now’ or a trait that is present ‘most of
the time’. This mirrors the dichotomy
between situational and dispositional aspects
of behavior that have been the subject of
much discussion by personality theorists and
behaviorists.

Emotion, mood, temperament

The state–trait dichotomy was certainly an
advancement upon vernacular labels for
affect, and it soon caught on as a practice in
psychological research to describe both state
and trait when assessing anxiety, anger, or
depression. However, the state–trait instru-
ments are limited by some ambiguities
(Fernandez, 2002). Asking subjects to report
how they feel ‘right now’ still leaves unclear
the distinction between emotion and mood,
both of which may be present at a point in
time and hence get subsumed under ‘state’.

Similarly, asking how a person ‘feels gener-
ally’ may elicit answers that could pertain
to either mood or trait because both mood
and trait share the property of taking up
more time. Clearly, the domain that is most
obscured by the state–trait distinction of
affect is mood.

A further improvement would be to refine
the dichotomy into a trichotomy which
allows for any affective quality to assume the
form of an emotional episode, a mood state,
or a temperamental trend (Table 32.1). The
first of these three forms represents a rela-
tively sharp and short-lived change in affec-
tive intensity, the second represents a
medium-term duration of affect, and the third
represents the recurrent frequency of a par-
ticular affect. These in turn correspond to
the phasic, tonic, and cyclic properties of all
affect. Emotion occurs as an episode and is
therefore phasic, mood persists and is there-
fore tonic, and temperament is the recurrence
of a particular emotion and therefore has a
cyclic quality.

These three different forms of any affec-
tive quality are sometimes reflected by the
semantic variations within many languages.
In English for example, when a person
becomes angry, that condition may be
labeled anger or fury; when the anger persists
for an extended time, the person may be said
to be in a ‘crabby’ or irritable’ mood, whereas
one who is habitually angry may be deemed
a hostile or fractious person (Table 32.1).
Language, however, turns out to be a crude
instrument for labeling affect because of
numerous individual differences in word

662 THE SAGE HANDBOOK OF PERSONALITY THEORY AND ASSESSMENT

Table 32.1 Emotion, mood, and
temperament forms of affect

Affective form
Affective
quality Emotion Mood Temperament
Fear Afraid Anxious Nervous
Anger Angry Irritable/ Hostile

irascible
Sadness Sad Depressive/ Melancholic

dysthymic

9781412946513-Ch32 5/12/08 3:25 PM Page 662

usage and the fact that any single language
has its fair share of gaps and redundancies in
labeling phenomena.

CORE OF NEGATIVE AFFECT (CONA)
IN MEDICAL POPULATIONS

In our present review of the research on
anger, fear, and sadness in medical popula-
tions, it was not always possible to clearly
delineate what was emotion from what was
mood-related, or temperament but we do
regard this tripartite form of affect as a nec-
essary frame of reference for future research
in this field. Another obstacle to firm conclu-
sions in this endeavor was the uncertainty of
the exact role or influence played by each
affective type within each medical condition.
As in the context of pain, affect could be a
precipitant, a predisposing factor, an aggra-
vator, a perpetuating factor, a consequence,
or just a correlate (Fernandez, 2002). With
regard to the last of these, it would also
help to know if we are referring to co-
occurrence, covariance, or equivalence
between two variables. This is another pro-
posed extension of our methodological
approach to studying affect in illness, even
though past literature may not lend itself to
such a level of discrimination.

Surveying the last five years of published
research, we set out to find studies of CVD,
cancer, and HIV/AIDS in which all three
core components of negative affect had been
investigated. The product was a handful of
studies quite divergent in terms of their
design and their hypotheses. Nevertheless,
these studies mark the beginnings of a new
line of enquiry into the CONA and they are
therefore the subject of review in the accom-
panying section.

Cardiovascular disease (CVD)

In an extensive narrative review of CONA in
coronary heart disease (CHD), Suls and

Bunde (2005) reported (1) evidence for
depression in the development (precipitation)
of CHD; (2) some evidence for depression
leading to disease progression (exacerbation)
in CHD; (3) evidence for anxiety in the
development of CHD; (4) meager evidence
for anxiety in the progression of CHD;
(5) some evidence for hostility in the deve-
lopment of CHD; and (6) minimal evidence
of hostility in the progression of CHD. This
means that anxiety, depression, and anger are
primarily precipitants rather than aggravators
of CHD. This is only in partial agreement
with the findings on pain, where anxiety is a
definitely a precipitator, depression is largely
a consequence, and anger is at least a corre-
late of pain. Suls and Bunde do not comment
on the relative or collective effects of the
triad of emotions on CHD because of insuffi-
cient research on all three affective qualities
within the same samples.

Mixed results in the review by Suls and
Bunde may be due to methodologically diverse
studies – especially the use of different meas-
ures of affect across studies. Also, Suls and
Bunde relied on significance levels rather than
effect sizes to reach their inferences. Their
interpretations that anxiety and depression (but
not anger) are related to increased CHD risk in
healthy samples may be re-evaluated on close
inspection of their data as summarized in
Table 32.2. As shown in the table, the actual
percentage of studies reporting significant rela-
tionships between affect and development of
CHD never deviated far from chance levels nor
did it differ appreciably across the three affec-
tive types: 53% for depression, 42% for anxi-
ety, and 48% for anger (Table 32.2). The role
of depression as an aggravator of existing
CHD is unclear due to what the authors
identified as negative significant effects.
Unfortunately, the exact number of negative
significant effects was not specified. Other
than that, the percentage of studies reporting
significant aggravation of CHD by affect is
remarkably similar: 29% for anxiety, and 27%
for anger. Despite these findings, the role of
anger (relative to its CONA counterparts) is
seemingly understated in the etiology of CHD.

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It must also be pointed out that Suls and
Bunde used measures of anger expression as
predictors of CHD prognosis, when alterna-
tively anger inhibition has also been impli-
cated in CHD (Brosschot and Thayer, 1998;
Magai et al., 2003; Smith and MacKenzie,
2006). The suppression or internalization of
anger may demand greater cognitive effort
and involve vagal mechanisms that increase
the risk of cardiovascular deterioration.
Given that many of the studies reported used
the STAXI to assess anger, a distinction
could have been made between internalized
and externalized anger.

A subsequent study by Kubzansky et al.
(2006) appeared in response to the limitations
of previous research in which anxiety, depres-
sion, and anger had been measured either
singly or else as parts of a broader construct.
The authors proposed a measure of general
distress common to anxiety, depression, and
anger in addition to orthogonal measures that
were termed ‘iso-anxiety’, ‘iso-depression’,
and ‘iso-anger’, respectively. They turned to
the MMPI-2 which has 72 items that make up
three content scales for measuring anxiety,
depression, and anger, respectively. Responses
to these 72 items were extracted from a

sample of 1,306 men who had completed the
MMPI-2, and these data were subsequently
analyzed using principal factor analysis with
orthogonal varimax rotation. Based on this,
three near-orthogonal scales were created for
measuring the three corresponding affective
types. Additionally, a fourth ‘general distress’
scale was constructed to include items that
loaded equally strongly on more than one
factor. The same sample of men was followed
up for an average of 11 years at which point
the MMPI-2 was re-administered. Data were
analyzed in terms of multivariate-adjusted rel-
ative risks of CHD for those highest versus
lowest on each of the scales. Results showed a
strong association between general distress
and the incidence of CHD. Iso-anxiety was
significantly associated with CHD outcomes,
especially for myocardial infarction; iso-anger
was associated primarily with angina pectoris;
and iso-depression was not significantly asso-
ciated with any CHD outcome. The authors
concluded that their results call for an appreci-
ation of the shared as well as unique contribu-
tions of negative emotions in the development
of CHD.

It should also be noted that independent
investigations have shown that acute outbursts

664 THE SAGE HANDBOOK OF PERSONALITY THEORY AND ASSESSMENT

Table 32.2 Number of studies showing affective influences on coronary heart disease (CHD),
based on Suls and Bunde (2005)

Significance of effect
Marginal or select

Direction of effects Significant significant Non-significance Total
Depression → CHD 10 7 2 19
Depression ↑ CHD 24 5 15 44
Anxiety → CHD 5 3 3 12 or 11
Anxiety ↑ CHD 4 1 9 14
Anger → CHD

Cynical hostility 5 2 4 11
Trait anger 1 1 1 3
Anger expression 5 2 2 9
∑ 11 5 7 23

Anger ↑ CHD
Cynical hostility 1 0 5 6
Trait anger 1 1 1 3
Anger expression 2 0 4 6
∑ 4 1 10 15

→ Precipitating factor
↑ Exacerbating factor

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of anger, fear, and sadness can trigger heart
attacks (Carroll et al., 2002; Kamarck and
Jennings, 1991; Lear and Kloner, 1996;
Mittleman et al., 1995). However, cardiovas-
cular reactivity is not only the result of nega-
tive affect and can be even greater during
intensely positive affect (Heponiemi et al.
2006). By implication, it is the sudden inten-
sification of arousal during emotion that
seems to be a precipitating factor in cardiac
incidents. In the long term, anger, depres-
sion, and anxiety may also encourage
other unhealthy behaviors (e.g. smoking)
that increase the risk of CHD (Smith and
Ruiz, 2002).

The role of multiple affective qualities in
cardiac incidents is also reflected in the rela-
tively new construct called vital exhaustion
(VE). As conceptualized by its originator,
this includes irritability, demoralization, and
fatigue (Appels, 1990; Appels and Mulder,
1988a, 1988b). Here, elements of anger and
sadness are combined with fatigue. The
anger seems to be internalized rather than
externalized in people with this condition
(Bages et al., 1999). VE seems to overlap
partially with the type A personality which is
characterized as a pattern of hostility, impa-
tience, and competitiveness (Rosenman
et al., 1975). It is quite possible that the
fatigue and depression of VE may actually be
a byproduct of (prolonged) type A-related
behavior. In terms of life events, sustained
job stress/conflict, unemployment, and
bereavement have been known to culminate
in VE (Falger and Schouten, 1992).

Whatever its bases, VE was initially
regarded as a precipitator of myocardial
infarction (Appels, 1990). It has also been
shown to be associated with angina pectoris
(Appels and Mulder, 1988a) and cardiac
events following angioplasty (Kop, 1995). It
is not a stretch to find the depressive and
anergic elements of VE following serious
cardiac incidents.

It bears mentioning that both the type
A and VE constructs have had their share of
mixed results in their relationship to CVD
(e.g. Miller et al., 1991). This is not surprising

given the curious admixture of somatic,
affective, and behavioral features within
these constructs. Nonetheless, what is
common to both constructs is a role of affect,
even though VE emphasizes depression and
type A emphasizes anger. Yet other psycho-
logical investigations have revealed a part
played by anxiety in CVD (e.g. Barger and
Sydeman, 2005; Herrmann-Lingen and Buss,
2007). In sum, it pays to go in search of all
three of these core affective qualities, keep-
ing in mind that each may enter the picture
through a different pathway, namely as
precipitator, exacerbator, consequence, or
perpetuator of CVD.

Cancer

Almost opposite to the anger-prone type
A personality that is implicated in CHD, a
personality prone to repressing negative emo-
tions was articulated (Temoshok, 1987). Such
non-expression of negative affect was sus-
pected as a factor in the etiology of cancer. It
came to be known as the type C personality.

Lieberman and Goldstein (2006) therefore
investigated whether the ventilation of anxi-
ety, depression, fear, and anger would have
an impact on depression and quality of life in
patients already diagnosed with breast
cancer. The patients engaged in emotional
expression through the medium of Internet
bulletin boards for a period of about six
months. The use of negative emotional words
in each of the affective categories was exam-
ined in relation to the dependent measures.
Regression analyses revealed that anger
expression was associated with improved
quality of life and reduced depression, thus
hinting at the psychodynamic notion of
depression as anger turned inward. However,
the expression of anxiety or fear was associ-
ated with increased depression and reduced
quality of life. The expression of sadness was
not significantly related to the outcome
measures. While these findings by no means
show that suppression of negative affect
causes cancer, they encourage the view that

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unexpressed anger is related to psychosocial
impairment in breast cancer.

More extensive coverage of the research in
this area was achieved in a meta-analysis by
McKenna et al. (1999). Aggregating effect
sizes across 46 studies, they found only a
modest relationship between the presence of
anxiety, depression, or anger (or their equiva-
lent temperaments) and the development of
breast cancer. The average effect sizes did not
exceed 0.38 even when some of the depend-
ent measures were combined into a broader
construct of emotional denial/ repression.

A recent prospective study by Tijhuis et al.
(2000) attempted to find out cancer incidence
and mortality as a function of emotional con-
trol of anxiety, depression, and anger. Almost
a thousand men born between 1900 and 1920
and living in Zutphen, Netherlands were
examined medically for cancer and also
interviewed and assessed using the Courtald
Emotional Control Scale (CECS) (Watson
and Greer, 1983) in 1985, 1990, 1993, and
1995. Focusing on a final sample of 590 men,
it was found that from 1985 to 1995, 119 of
them were diagnosed with cancer and 71
died of cancer. Descriptive statistics for the
sample revealed the highest level of emo-
tional control for anxiety (19.2), a slightly
lower level for depression (18.4) and a
slightly lower level for anger (16.4) with
almost equivalent degrees of variability.
When Cox proportional hazards models were
used to determine effects of emotional con-
trol on cancer incidence and mortality, it was
found that men within the highest and inter-
mediate tertiles of controlled depression had
a significantly increased risk of cancer mor-
tality even after adjustment for other risk fac-
tors such as age, marital status, and SES; this
was not the case for men who suppressed
anxiety or anger. Control of depression was
also significantly related to cancer incidence,
but anger control or anxiety control were not.
This study is nevertheless informative
because it shows that cancer patients are con-
sumed not only by the somatic demands of
their disease but also by a struggle to control
anxiety, anger, and depression even though

only one of these (when controlled) seems to
increase the incidence and mortality associated
with cancer.

Also using the CECS, an Australian study
on breast cancer failed to find any significant
associations between cancer outcome and
emotional suppression of any kind, before or
after controlling for age effects (O’Donnell
et al., 2000). Once again, the more important
message for our purposes is that the cancer
patients did seem to experience components of
the core of negative affect, as implied by their
scores on emotional control for each of these.

A qualitative illustration of core compo-
nents of negative affect in cancer patients is
visible in some of the nursing literature. For
example, Bowers et al. (2002) mention that
even though many women with cervical
cancer were depleted of physical energy, they
would utter statements such as:

That was once in my mind I was angry. I wanted to
get in and get over with as soon as possible and
not wait a month. Then I was too weak and tired
to display it very much … One day I came home
and went to bed. The longer I laid in bed the
madder I got. I was screaming to myself. I thought
I would call a friend, but I did not want to dump
on her so I said I can’t take it anymore and I came
downstairs and banged and slammed and got
supper. (2002: 144–145)

A further quote by Bowers et al. captures
the almost existential anxiety of the cancer
patient: ‘There is a reason for everything. I
don’t know it is. I don’t know why. I began to
think. Get a grip on yourself and find a pur-
pose’ (2002: 139). A final quote by Bowers
et al. captures the despair/depression of the
cancer patient. ‘I guess my life was interest-
ing, with so many things, and now it is not.
Life is destroyed. It was so good before’
(2002: 145). These anecdotes help remind us
of the cognitive appraisals that underlie the
statistical data on anxiety, depression, and
anger in medical populations.

HIV/AIDS

One of the few recent empirical studies of
the CONA in the context of HIV/AIDS was

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conducted by Atwine et al. (2005). In a rural
district of southwestern Uganda, 123 chil-
dren aged 11–15 years old whose parents had
reportedly died from AIDS were compared
with a normative sample of 110 children of
similar age and gender living in intact house-
holds. They were all administered an appro-
priately translated version of the Beck Youth
Inventories (Beck et al., 2001) which had
been designed as a diagnostic aid for anxiety,
depression, and anger, and for self-concept
problems and disruptive behavior in youth.
Results revealed significantly higher levels
of disruptiveness as well as all three compo-
nents of CONA in the orphaned group as
compared to the non-orphaned group.

Another group of researchers (Teva et al.,
2005) studied 100 HIV/AIDS patients
between 18 and 70 years old who were
recruited from various hospitals in Andalusia,
Spain. They were administered a battery of
tests suitable for assessing the CONA: the
BDI, the STAI, and the STAXI. It was found
that most of the 63 men and 37 women in the
group reported low levels of state anger, with
about one-third not expressing anger. This
may be related to the additional finding that
most participants were low in trait anger to
begin with. Anger was higher during the
symptomatic stage as opposed to the pre-
symptomatic stage. Similarly, anxiety was
greater during lypodystrophy than before it.
Anxiety was far more prevalent in men than
women. Most men also showed some depres-
sion but most women did not. The authors
explain these differences with reference to
cognitive appraisals that differ according to
gender and stage of infection.

To the extent that pain is often a symptom
in HIV/AIDS, the kinds of affective distress
observed in chronic pain patients are also
likely to manifest in HIV/AIDS patients
(Marcus et al., 2000). Morever, HIV/AIDS
patients, like cancer patients, often go through
stages of adjustment to this (presumably) ter-
minal illness. In the traditional model of
Elizebeth Kubler-Ross (1974, 1997), this
begins with shock and anxiety, proceeds to
anger, and ends in depression. The core

components of negative affect may thus unfold
in sequence rather than appear concurrently.

Pain

Over the last half of the twentieth century,
considerable research accumulated on anxi-
ety, depression, and (to a lesser extent) anger
in pain patients. This has already been cri-
tiqued and synthesized (e.g. Banks and
Kerns, 1996; Fernandez, 2002). We now turn
to a few recent empirical articles on the core
of negative affect in pain, followed by a dis-
cussion of how this parallels the experience
of negative affect associated with cancer,
HIV/AIDS, or CVD.

Feeney (2004) evaluated 100 post-surgical
orthopedic patients above the age of 65.
These individuals were administered the
geriatric depression scale (stripped of its
somatic items because these do not discrimi-
nate between depressed elderly and non-
depressed elderly). Patients were also
administered the STAI and the STAXI to
generate state and trait measures of anxiety
and anger, respectively. The McGill Pain
Questionnaire (MPQ) (Melzack, 1975) was
used to derive a total pain score by summing
the rank values of pain descriptors endorsed
by patients. The authors found that pain was
significantly correlated with state anxiety
and depression but not with any of the meas-
ures of anger. Standard multiple linear
regression analysis revealed that about 31%
of the variance in total pain was explained by
the five affective variables but only state anx-
iety had a significant standardized weight,
accounting for about 18% of the variance in
pain. The remaining four variables did not
contribute significantly to the prediction of
pain over and above that accounted for by
state anxiety. This is quite likely due to the
particular pain measure that was chosen. In
using the rank values of pain descriptors
from the MPQ, the authors were opting for a
crude index of pain in comparison to the
scales of the multidimensional pain inventory
(Kerns et al., 1985) or even other measures

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offered by the MPQ itself such as the present
pain intensity. The MPQ also allows meas-
urement of affective pain as a separate factor,
and examining this variable would probably
have led to more significant results beyond
those witnessed for anxiety.

Ghosh and Puja (2004) administered the
BDI, STAI, and STAXI to 50 female outpa-
tients with migraine headache and an equally
sized group of age-matched females with no
headaches. T-tests showed significantly
higher scores for the patient group on six
measures (trait anxiety, trait anger, anger-in,
anger-out, anger control, and depression).
The significant differences on trait rather
than state anger and anxiety raise the likeli-
hood that patients’ headaches were not pre-
cipitated by affective episodes but were
predisposed by affective temperaments. This
conclusion is consistent with the findings of
several studies cited by the authors.

In a more specific investigation of anger
expression styles as they relate to pain, Kerns
et al. (1994) found that anger-in and anger
expression are correlated with chronic low
back pain severity, though the former is associ-
ated with poorer adjustment. Similarly, Bruehl
et al. (2002) found that both anger-in and
anger-out affected pain sensitivity, but only the
latter seemed to be mediated by impairment in
antinociceptive effects of endogenous opioids.

In a broader investigation of the inhibition
and expression of multiple emotions, Burns
et al. (2003) randomly assigned students to
three conditions: anger, sadness, and joy,
respectively. In each condition, subjects
recalled and described a recent event that
evoked the relevant emotion. This was
accompanied by a cold pressor pain test. Pain
response was assessed by temporal measures
of threshold and tolerance as well as by
verbal descriptors on the MPQ. Unlike other
findings by the same authors, a significant
positive association was found between
anger-out and pain threshold (but not pain tol-
erance or MPQ scores); this effect was paral-
leled by decreases in systolic blood pressure
but not diastolic blood pressure. In contrast,
induced sadness led to the largest increases

in MPQ scores of pain severity. It would be
interesting to extend this line of enquiry by
investigating any changes in pain sensitivity
that might occur when fear is evoked using
the same recall procedure as used for anger
and sadness.

Our understanding of the emotions experi-
enced by those in pain can be further deepened
by an exploration of how exactly their pain is
interpreted. As pointed out earlier, beneath the
statistical data on negative affect are undercur-
rents of cognitive appraisals about the medical
condition. Thus, pain patients are less likely to
be angry at the pain itself and more likely to be
angry at the ramifications of their painful con-
dition (Fernandez and Turk, 1995). Similarly,
anger is to be expected in any disease or disor-
der that is diagnostically ambiguous, refractory
to treatment, (mis)attributed to psychological
mechanisms, financially burdensome, and
legally fractious. Consider the emotional reac-
tions that arise in cancer patients. As Bowers,
Tamlyn, and Butler mention,

Most women experienced anger not at the cancer
itself, but rather in relation to the communication
and contact with others as they lived with ovarian
cancer. In general, the causes of women’s anger
were related to misdiagnosis, late diagnosis, multi-
ple testing, physicians discounting their symptoms
and/or waiting for treatment, or inaccessibility to
prompt treatment. (2002: 144)

Just as chronic pain can generate life inter-
ference which culminates in depression
(Rudy et al., 1988), so can cancer, CVD or
HIV/AIDS become depressing via their lim-
iting effects on day-to-day functioning. The
process of functional decline can be met with
considerable apprehension, worry, and out-
right dread, especially if death is imminent. It
is therefore not far-fetched to also consider
the existential anxieties that are probably
added to the other objects of anxiety, depres-
sion, and anger in these patients.

CONA COMORBIDITY

The preceding literature review shows that
anxiety, depression, and anger do exist

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(sometimes in isolation, sometimes in com-
bination) to a clinically significant degree in
patients with medical ailments. The next
issue concerns the extent to which the com-
ponents of CONA are comorbid with one
another, and what corollaries arise thereby.
Across studies of medical patients, there
have been repeated observations of a close
relationship between anxiety and depression.
The association between anger and each of
its two counterparts in the CONA has been
less researched. Given that anger and fear are
twin emotions that mobilize the individual to
fight or flight, it is likely that in any set of
nomothetic data from individuals facing
provocation or danger, there would be traces
of both anger and fear. Up to this time,
however, the emphasis has been on the so-
called comorbidity between anxiety and
depression.

Comorbidity statistics

Comorbidity, at its simplest, refers to the co-
occurrence of two disorders in the same indi-
vidual. However, it makes a difference
whether the individual is evaluated for
episode comorbidity or lifetime comorbidity.
The former refers to multidiagnostic co-
occurrence at one point in time. This is likely
to be exceeded by the latter which means
multiple diagnoses occurring at any point in
the individual’s lifetime. Based on an exten-
sive epidemiological study, Robins et al.
(1991) reported a 60% lifetime comorbidity
of psychiatric disorders. About one-third of
patients diagnosed with anxiety disorder
were also diagnosed with a depressive disorder
(Sanderson et al. 1990).

Going beyond co-occurrence to correla-
tion, the picture remains similar. Anxiety and
depression have been repeatedly shown to
co-vary in a positive direction. Dobson’s
(1985) review of the relevant literature
showed that the correlation between scores
on anxiety and scores on depression ranged
from +0.27 to +0.94, with an average corre-
lation of +0.61. This average correlation was

only a little less than the average correlation
of +0.66 between anxiety scales.

Principally, there are five main explana-
tions for comorbidity of the core components
of negative affect: definitional overlap,
instrument overlap, response set, misinter-
pretation of data, and phenomenological
bases. It is necessary to evaluate the tenabil-
ity of each of these explanations as they have
implications for the theoretical and applied
potential of CONA as a construct.

Definitional overlap

The definitional overlap pertains to a similar-
ity of conceptualization, in this case, between
multiple diagnostic labels. Specifically, if
there are similarities in the way anxiety and
depression are operationally defined, it
would not be surprising that when one is
identified, so is the other. For the definition
of clinically significant depression and anxi-
ety, we turn to the Diagnostic and Statistical
Manual of Mental Disorders, version IV-Text
Revision (DSM-IV-TR). In this nosological
system, the dysthymic variant of depressive
disorder comprises at least two years of at
least two of the following symptoms: poor
appetite, sleep disturbance, fatigue, low self-
esteem, poor concentration, and perceived
hopelessness. By comparison, generalized
anxiety disorder (GAD) comprises more than
six months of worry/anxiety with at least
three of the following symptoms: restless-
ness, fatigue, difficulty concentrating,
irritability, muscle tension, and sleep distur-
bance. As immediately apparent, 50% of the
symptoms of dysthymia are found in GAD
and vice versa. This degree of overlap may
account for the high comorbidty between
these two disorders, as reported in the
National Comorbidity Survey (NCS) of
8,000 respondents across the US (Kessler et al.,
1994). In this study, the six-month comorbid-
ity of GAD and dysthmia was quantified by
an odds ratio of 21.5, odds ratio being the
ratio of frequency of two disorders being
simultaneously present or absent to the

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frequency of each one being present on its
own – in other words, the ratio of a joint
occurrence to a singular occurrence. The
GAD–dysthymia odds ratio was among the
highest for any pair of psychiatric disorders.
Similarly high odds ratios have been reported
for other pairs of anxiety and depressive dis-
orders, in particular panic disorder and major
depression, with an odds ratio of 21.3 in the
Epidemiological Catchment Area (ECA)
study of 20,000 respondents in five US com-
munities (Robins et al., 1991). In fact, the
average pairwise associations between affec-
tive disorders (inclusive of mania) and anxi-
ety disorders have been higher than that
between anxiety disorders (Kessler, 1995). In
short, the overlap of DSM diagnostic criteria
may account for some of the comorbidity
between clinical anxiety and depression.

Instrument overlap

The idea that high comorbidity between anx-
iety and depression could be due to instru-
ment overlap occurred to various scholars
who noticed that many psychological tests
discriminated poorly between the two affec-
tive types (Clark et al., 1990). The same
applies to the comorbidity of anger, depres-
sion, and anxiety. An inspection of the items
in the Beck Depression Inventory (BDI)
(Beck and Steer, 1993a) and the Beck
Anxiety Inventory (BAI) (Beck and Steer,
1993b) reveals similarity of content as does a
comparison of the STAI and STAXI. Anxiety
and anger are likely to share common physi-
ological reactivity by virtue of their common
roots in sympathetic activation. However, if
psychological tests rely on the subjective
feelings as defining features of these syn-
dromes, then they are less likely to generate
overlapping profiles.

Interpretation of data

Suls and Bunde (2005) adopt Watson’s view
that the frequent correlation in self-report

data for anxiety and depression must be
partly rooted in a common latent factor or
shared underlying dimension called negative
affect. Reacting to this multicollinearity,
Ketterer (1996) and others have suggested
that we replace the measurement of anxiety,
depression, and anger as separate entities
with a global measure of negative affect.

Certainly, multicollinearity between vari-
ables (especially if it exceeds 0.80) suggests
redundancy. But just because entities are cor-
related does not mean that they are con-
nected. It simply means they co-vary. Thus,
the strong collinearity between height and
weight is not grounds for collapsing the two
into one construct. Culture is closely associ-
ated with race, but it still makes much sense
to tease the two apart. Verbal and quantitative
IQ tend to be highly correlated, yet they are
often viewed as distinct areas of ability.

A useful lesson in the interpretation of
multicollinearity can be found in the context
of measuring pain components. Turk et al.
(1985) discovered that in a multiple-group
confirmatory factor analysis, the sensory,
affective, and evaluative subscales of the
MPQ turned out to be highly intercorrelated:
r = 0.81 between sensory and affective,
r = 0.67 between affective and evaluative,
and r = 0.64 between evaluative and sensory,
thus yielding an average correlation of 0.71
among the three constructs. Moreover, the
cross-construct correlations exceeded the
within-construct correlations. The authors
took this as a sign of lack of distinctiveness
of the subscales and therefore recommended
using the total factor score rather than
individual scores on the three subscales.
However, in a rebuttal, Melzack adduced
several bits of evidence from perceptual psy-
chophysics to show that a high correlation
among variables is not a sign of redundancy
and does not necessitate collapsing the vari-
ables into one. Specifically, increases in light
intensity are associated with enhanced dis-
criminability of color, contours, texture, and
distance, yet we do not suggest conflating
color and texture into one variable. Similarly,
in audition, increased volume enhances

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discrimination of pitch, timbre, and spatial
location, but this is not grounds for abandon-
ing separate measures of timbre and pitch
(Melzack and Katz, 1992).

Response set

In an extensive and seminal review paper,
Russell and Carroll (1999) strongly disputed
the idea proposed by Watson et al. (1988)
that positive and negative affect are inde-
pendent unipolar dimensions. In the process,
Russell and Carroll also offered empirical
data and reasoned arguments that now allow
us to seriously doubt the value of collapsing
anxiety and depression (and anger for that
matter) into an undifferentiated phenomenon
called ‘negative affect’. Citing the classic
work of Bentler (1969), it was pointed out
that spurious correlations can emerge from
self-report tests when there is an acquiescent
response style in test-taking. Russell and
Carroll then went on to cite about a dozen
other studies containing empirical evidence
of how this acquiescent response set has in
fact influenced measures of affect. This may
well account for the frequently observed cor-
relations between self-report measures of
anxiety and depression as well as of anger.

Phenomenological bases

Of course, anxiety, depression, and anger (or
their corresponding emotions of fear, sad-
ness, and anger) often co-occur, but this is
not sufficient grounds for resorting or revert-
ing to a general concept of ‘negative affect’.
Some of the association is phenomenologi-
cally based. First, at any point in time, each
of the three emotions may be rooted in quite
different events: a patient may be angry
because of conflict on the job, depressed
because of illness, and anxious about the
welfare of family members. Second, the
same things that make people depressed can
also make them anxious and angry. Failure in
a task/test often leaves one feeling sorry or

sad for oneself, angry at the person evaluat-
ing one’s performance, and worried about the
consequences for one’s goal attainment.
Killing of an admired leader often leads to
sorrow for the leader’s suffering or depriva-
tion of rights, anger toward the killers, and
apprehension about how to cope without the
leader. Popularly called ‘mixed emotions’,
these co-occur because of different appraisals
of the same event. So, co-occurring emotions
can be due to (1) different reactions to differ-
ent event or (2) different reactions to the
same event. It would not make sense to com-
bine such multiple emotions into one amor-
phous ‘negative affect’ because these emotions
originate from quite different circumstances or
else are differentiated by separate appraisals of
the same event.

MEASURING CORE COMPONENTS
OF NEGATIVE AFFECT

The current componential representation of
negative affect is consistent with a major per-
spective in affect science called differential
emotions theory or the theory of discrete
emotions. Accordingly, the tests used to
assess the core components of negative affect
should be selected to allow the differentiation
of negative affect into its core components of
fear, sadness, or anger, or their respective
clinical equivalents of anxiety, depression,
and anger. The options for assessing these
types of affect would therefore exclude the
positive and negative affect scales (PANAS)
(Watson, et al., 1988) which are predicated
on a view of undifferentiated negative affect.
Moreover, the single word descriptors that
make up the PANAS (distressed, upset, hos-
tile, irritable, scared, afraid, ashamed, guilty,
nervous, and jittery) are gross labels that are
unsuited for accessing the underlying
appraisals of each emotion. If anything, this
is what may obscure some of the fine differ-
ences among anxiety, depression, and anger
or their emotional equivalents of fear, sad-
ness, and anger. It should also be noted that

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the concept of negative affect as proposed by
Watson et al. does not pertain to negative
emotions as much as ‘subjective distress and
unpleasurable engagement that subsumes a
variety of aversive mood states, including
anger, contempt, disgust, guilt, fear, and
nervousness, with low NA being a state of
calmness and serenity’ (1988: 1063).

The use of single-word adjectives for
assessing affect dates back to the multiple
affect adjective checklist (MAACL)
(Zuckerman and Lubin, 1965) – revised as
the MAACL-R (Zuckerman and Lubin,
1985). This instrument does generate scores
for anxiety, depression, and hostility but its
factor structure is still an unsettled matter
(e.g. Gotlib and Meyer, 1986). Another
instrument of the same genre is the profile of
mood states (POMS) (McNair et al., 1981)
which lists 65 adjectives of affect to be rated
on a four-point scale of amount/frequency.
Subscale scores are generated for all three
components of the CONA in addition to three
other subscales pertaining to energy levels
and cognitive function. Psychometrically,
it has received some support though ques-
tions remain about how to interpret its results
(e.g. Boyle, 1987).

The differential emotions scale (DES-IV;
Izard et al., 1974) takes affect assessment a
step deeper by replacing single word adjec-
tives with actual statements that better reflect
the experience of emotion. Subscale scores
are generated for 11 types of affect, among
them anger, fear, and sadness. There has
been limited psychometric evaluation of the
DES-IV although some of the empirical
outcomes are encouraging (Boyle, 1986).

Apart from the above instruments directed
specifically at affect, there are more general
tests such as the SCL-90-R and the MMPI-2.
Both of these are commonly used in health
psychology to cast a wide net for detecting
psychopathology. In the process, they allow
the identification of clinically significant
levels of the CONA. One special advantage
of these tests is that their psychometric valid-
ity and reliability have been the subject of
extensive research and are now fairly well

established. However, they are broad in
scope and therefore bring in more data than
is needed for our current goals of assessing
negative affect.

CONCLUSION

It is the thesis of this chapter that there are
three key components to negative affect: fear,
sadness, and anger, which can take the form
of emotions, moods, or temperaments.
Previous research has studied them mainly as
discrete emotions or else as the clinical syn-
dromes of anxiety, depression, and anger.

The three core components of negative
affect have an evolutionary history that has
earned them special roles in survival. In par-
ticular, they are part of the individual’s reper-
toire of defenses against threat, attack, or
adversity in general. Thus, anxiety, depres-
sion, and anger are prevalent in medical pop-
ulations such as those afflicted with CVD,
cancer, or HIV/AIDS. Research has pointed
to the comorbidity of these affective types.
The frequent co-occurrence or covariation of
these affective types does not mean that they
should be collapsed into one broad category
called negative affect. Close scrutiny has
revealed that the comorbidity is in part due to
overlap in nosological criteria for anxiety
and depression and in part due to overlapping
items across psychological tests. The comor-
bidity may also be an artifact of response
sets. Most important, anger, fear, and sadness
are linked by unique threads of cognitive
appraisals in response to the same situation
or else by multiple appraisals in response to
multiple stimuli.

Future research may benefit greatly from
the assessment of the three core components
of negative affect in medical populations.
This is not strictly tied to any premise that
anxiety, depression, and anger co-occur, co-
vary, or are equivalent. Rather, the prime
reason is that there is a high probability
of one or more of these affective types
in anyone who faces adversity. Perhaps,

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by including three subscales on one and the
same test of affect, scores for anxiety, depres-
sion, and anger can be output on the same
metric, yet on three separate but parallel con-
tinua. This would enable the charting of a
profile of the individual’s core components
of negative affect. Protracted over time, such
a chart might also reveal patterns that allow
us to differentiate the emotional, mood-
related, or temperamental aspects of anger,
fear, and sadness. In this way, the landscape
of a person’s affective function can be better
mapped to identify, with greater specificity,
the areas in need of clinical attention.

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